Let's talk about sales velocity. Off shelf-detection algorithms are based on identifying a significant gap between what you expected to sell and what you actually sold (at a product-store level). The basic system I described in "Off-Shelf Alerting tools are simple" will call out an alert when, for a period of time, you sold nothing, but expected to sell at least 5 units. So how long should it take the average product at the average store to sell 5 units? Rather longer than you might think.
The majority of supermarket grocery products sell less than 1 unit per store per week. I have seen this borne out repeatedly in practice and it’s supported by other studies. While I expect this feels wrong to you - you probably buy enough milk every week for this to seem wrong - it's real and has a huge impact on the value of off-shelf alerting, so please bear with me.
I can't share real data with you but I can generate something instructive from 2 key facts
- The average 50,000 product grocery store sells only 27,000 products each week (see the 2008 study A Comprehensive Guide to Retail Out-of-Stock Reduction in the Fast-Moving Consumer Goods Industry ). Each store does of course sell rather a lot of a few of these products but it does not come close to selling all of them each week.
- Unit sales by product, roughly follows a Pareto curve. (20% of the products generate 80% of the sales). Something like this:
Now, with a little math-magic I can figure out the average sales velocity for each 1% of the product range so that both these facts hold true. The following results aren’t exact of course – some stores sell more than others, some have more products, some hold more closely to the Pareto assumption – but the results are representative and more importantly, instructive.
This is what Unit Velocity (unit sales per store per week) looks like. There are a handful of very high velocity items (milk, some produce, fresh goods, soft-drinks, water, heavily-promoted items), but the vast majority clearly sell fewer, far fewer, than 10 a week.
Let’s look at that long tail in a little more detail: The bottom 90% of products sell less than 3.5 units per week and over 50% of products sell less than 1 unit per store per week.
What does this mean for off-shelf detection algorithms?
Remember, the basic off-shelf decision rule is that we will call out an alert when you should have sold at least 5 units but actually sold none. With that in mind:
- There are a few (very, very high-volume products) that could conceivably generate alerts on an hourly, intra-day basis. My guess though is that if you really are out of milk, your customers may let you know faster than the algorithm can spit out a report.
- Very few products have enough velocity even on a daily basis that 1 day of zero-sales is enough to flag an alert.
- The “average product” will take weeks of zero sales before you can call an alert with any confidence.
- Very low volume products may take so long it’s really not worth the effort in trying – thank goodness you haven’t lost too many sales waiting for that alert.
Now, this does not negate the value of off-shelf detection tools but it does help put things into context as to what they can (and can’t) do.
- Being able to generate alerts slightly earlier in the day is of very little value to you. Things just don’t change that fast.
- Hourly data is of very little value other than for a handful of very high-velocity items (and I strongly suspect these will self-heal at store level before you can even plan any kind of external intervention).
- Daily data is useful for the top 40%-50% of products, for all others weekly data is fine.
- It should come as no surprise that off-shelf detection algorithms generate far fewer alerts than you find when you do a physical audit: many of the off-shelf events get fixed by store operations before an off-shelf detection algorithm is capable of spotting them.
This post is the eighth in a series on On-Shelf Availability.